D
eep forgery is a forgery technology generated on the
foundation of artificial intelligence deep learning, which mainly
forges other people's facial expressions and voices in real time
by using of artificial intelligence technology, and combines it
into new videos. In December,2017, a user named “deepfake”
posted a pornographic video impersonating a well-known
Hollywood actress on a foreign website “Reddit”, instantly
activating a carnival on the Internet. Deep forgery has been
applied in the film and television industry a few years ago,
creating special efforts scenes and makeup to achieve more
refined communication effects. However, at the same time, it
also provides opportunities for people with ulterior motives [10].
B. Opportunity: Overcome Difficulties
1) Automatic Writing of Disaster Weather News Based on
Artificial Intelligence
Based on current deep learning technology, we can sort out
and recognize the real-time disaster warning data and carry out
a series of classification. However, if edit these materials into
formal news that can be output directly so as to realize automatic
writing, a certain of technical difficulties still exists. Below is
mainly to carry out analysis in two perspectives:
• News writing has a certain degree of subjective thinking
and opinions of the writer, so as the disaster weather
news. Except information related to disaster warning, it
may still contain the prevention measurements, notices,
and subjective opinions under detailed situation. Taking
the most common strong convective weather as the
sample, except reporting the forecast, strength and
others of rainfall, it still needs to prepare some special
prevention measurements combined with the terrain,
topography, crowd density, morning and evening peak
travel of a specific area. All of these have opinions and
evaluation of subjective thinking. Therefore, the writing
of disaster weather news still needs a certain degree of
artificial assessment and modification.
• Achieve the technical challenges needed by automatic
writing. Based on above analysis, we know the key
point of realizing automatic writing is to realize the
consideration of factors with subjective thinking
patterns, which involve in humanities, environment,
geography, politics and other elements. Therefore,
sufficient data samples are needed, and then an
algorithm that can simulate news schemes with
appropriate subjective thinking should be available. So
the news writers can be completely replaced and editing
of news.
2) Timeliness of Disaster News
As we know, the special feature of disaster early warning
news and other news is that they have a certain of timeliness.
Due to it is involved in paying attentions to the safety of relevant
groups, and leaving enough time to make corresponding
prevention measures, therefore, it certainly has a request of
timeliness of disaster news.
First of all, with the help of artificial intelligence, compared
with traditional news writing, the semi self-help mode of early
warning of disaster weather news has been certainly increased
efficiency. However, with the special timeliness request of
disaster weather, it still faces the challenge of improving
efficiency. The details include below aspects:
• Improve the efficiency of computer’s calculation
efficiency and performance. Due to a huge amount of
historical data and samples is needed, deep learning of
disaster news requires large amounts of calculation
resource. Therefore, in the perspective of test and
business operation, both need higher amount of
calculation resources.
• Optimize storage structure and logic. For example,
based on the advantages of cloud calculation, apply
objective storage to separately store static data and
dynamic data. Meanwhile, realize the separation of read
and writing.
• Due to the disaster weather forecast has a request of
timeliness even to minutes and seconds, how to
optimize the algorithm and logic of deep learning, how
to quickly obtain sample data and complete initial
writing of news in the soonest time are the challenges
that need to be consistently optimized and improved.
R
EF
ERENCES
[1
] H. Chen, and L. Zhou, “From Wenchuan earthquake to Jiuzhaigou
earthquake: An analysis of changes in disaster news coverage,”
Journalism, no. 11, pp. 35-38+57, 2017, doi:10.15897/j.cnki.cn51-
1046/g2.2017.11.006.
[2] Q. Guan, M. Diao, and G. Q. Yao, “Teaching and practice of “intelligent
computing” course integrating geoscience content,” Chin. Geol. Educ., no.
3, pp. 38-42, 2021, doi:10.16244/j.cnki.1006-9372.20211014.002.
[3] J. Zhang, K. Xue, Yang, Zhipeng, F. Zhang, R. Zhang, J. Yang, and G.
Feng, “Application of artificial intelligence and internet of things in
atmospheric science,” Adv. Geophys., no. 1, pp. 94-109, 2022.
[4] L. Zhou, “Disaster governance for the era of artificial intelligence - a
multi-case based study,” Chinese Adm., no. 8, pp. 66-74, 2019,
doi:10.19735/j.issn.1006-0863.2019.08.08.
[5] T. T. Zhao, “Agenda setting of the media in natural disaster reporting,”
Youth J., no. 3, pp. 38-39, 2015.
[6] [6] H. Y. Wang, “On the innovation of financial media report of the
party newspaper in the event of natural disasters -- Take the new media
report of flood fighting and rescue in 2020 of Anhui Daily as an example,”
News World, no. 5, pp. 33-36, 2021, doi:10.19497/j.cnki.1005-
5932.2021.05.12.
[7] D. Shan, and Y. Shi, “Research on the application of data mining
technology in Internet information retrieval,” Sci. Technol. Bull., no. 3,
pp. 161-164, 2014, doi:10.13774/j.cnki.kjtb.2014.03.037.
[8] J. Jin, and P. Wang, “Research on image recognition algorithm based on
convolutional neural network,” Commun. Inf. Technol., no. 2, pp. 76-81,
2022.
[9] W. Niu, “The challenges of artificial intelligence technology on
journalism ethics and response strategies,” Master's thesis, Hebei
University of Economics and Business, Shijiazhuang, 2019.
[10] H. Li, “The criminal sanction of misuse of personal biometric information:
The example of artificial intelligence “deep forgery”,” Polit. Law Forum.,
no. 4, pp. 144-154, 2020.
235
Authorized licensed use limited to: Hong Kong University of Science and Technology. Downloaded on July 19,2023 at 08:10:07 UTC from IEEE Xplore. Restrictions apply.